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2fc58fd abea06b a2f53f8 29b6798 a2f53f8 29b6798 abea06b 28e6034 a2f53f8 abea06b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | from flask import Flask, request, jsonify
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import os
from app2 import predict_stock_codes
app = Flask(__name__)
# Load the prediction model
model = CustomModel()
# Define a function to prepare the data for prediction
def prepare_data(date):
# Get the historical data for the given date
data = bs.query_history_k_data_plus(
"sz.000001", # Shanghai Composite Index
"date,open,high,low,close,volume",
start_date="2005-05-30",
end_date=date,
frequency="d"
)
data_list = []
while (data.error_code == '0') & data.next():
data_list.append(data.get_row_data())
data_df = pd.DataFrame(data_list, columns=data.fields)
# Convert 'open' and 'close' columns to numeric type
data_df['open'] = pd.to_numeric(data_df['open'])
data_df['close'] = pd.to_numeric(data_df['close'])
# Filter out stocks that meet the conditions
data_df = data_df[(data_df["open"] >= 0.98 * data_df["close"].shift(1).fillna(0)) & (data_df["open"] <= 1.02 * data_df["close"].shift(1).fillna(0))]
data_df = data_df[(data_df["high"] == data_df["close"]) & (data_df["low"] == data_df["close"])] # limit-up condition
data_df = data_df[(data_df["open"]!= 0) & (data_df["close"]!= 0)] # exclude zero prices
# Scale the data using MinMaxScaler
scaler = MinMaxScaler()
data_df[['open', 'high', 'low', 'close', 'volume']] = scaler.fit_transform(data_df[['open', 'high', 'low', 'close', 'volume']])
return data_df
# Define a route to predict the top 5 stock codes
@app.route('/predict', methods=['POST'])
def predict():
date = request.json['date']
data_df = prepare_data(date)
if data_df.empty:
return jsonify({'error': 'No data available for the given date'}), 400
y_pred = model.predict(data_df)
top_5_stocks = predict_stock_codes(y_pred, data_df)
return jsonify({'top_5_stocks': top_5_stocks})
if __name__ == '__main__':
app.run(debug=True) |